期刊文献+

基因富集及meta分析筛选非小细胞肺癌发生发展关键基因的研究 被引量:3

Screening Key Genes Associated with the Development and Progression of Non- small Cell Lung Cancer Based on Gene-enrichment Analysis and Meta-analysis
下载PDF
导出
摘要 背景与目的非小细胞肺癌(non-small cell lung cancer,NSCLC)是全球最常见的恶性肿瘤之一,其发病遗传机制仍不清楚。本研究旨在筛选影响NSCLC发生发展的关键基因和通路,为NSCLC发病遗传机制及靶向治疗的研究奠定科学基础。方法运用基因组富集分析(gene set enrichment analysis,GSEA)以及对单套数据集单个基因元分析(meta-analysis,meta)的方法,筛选出在转录水平上影响NSCLC发生发展的关键通路和基因。结果通过GSEA和meta两种分析方法得出的通路中,重叠性较高的主要为粘着斑通路和细胞骨架肌动蛋白调控通路。在粘着斑通路中31个基因具有统计学意义(P<0.05);细胞骨架肌动蛋白调控通路中32个基因具有统计学意义(P<0.05)。结论粘着斑通路和细胞骨架肌动蛋白调控通路可能与NSCLC的发生发展有重要的联系,后续本研究小组将对这两条通路中的具有统计学意义的基因进行生物功能学上的验证。 Background and objective Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors; however, its causes are still not completely understood. This study was designed to screen the key genes and pathways re^ated to NSCLC occurrence and development and to establish the scientific foundation for the genetic mechanisms and tar- geted therapy of NSCLC. Methods Both gene set-enrichment analysis (GSEA) and meta-analysis (meta) were used to screen the critical pathways and genes that might be corretacted with the development and progression of lung cancer at the transcription level. Results Using the GSEA and meta methods, focal adhesion and regulation of actin cytoskeleton were determined to be the more prominent overlapping significant pathways. In the focal adhesion pathway, 31 genes were statistically significant (P〈0.05), whereas in the regulation ofactin cytoskeleton pathway, 32 genes were statistically significant (P〈0.05). Conclusion The focal adhesion and the regulation of actin cytoskeleton pathways might play important roles in the occurrence and development of NSCLC. Further studies are needed to determine the biological function for the positiue genes.
出处 《中国肺癌杂志》 CAS 北大核心 2012年第7期416-421,共6页 Chinese Journal of Lung Cancer
基金 广西科技攻关与新产品试制基金(No.10124001A-47)资助~~
关键词 肺肿瘤 基因富集 META分析 通路 关键基因 Lung neoplasms Gene set-enrichment analysis Meta-analysis Pathway Key gene
  • 相关文献

参考文献20

  • 1Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin, 2011,61(2): 69-90.
  • 2Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res, 2007, 35(Database issue): D760-765.
  • 3Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc NatiAcad Sci USA, 2005,102(43): 15545-15550.
  • 4Greenbaum D, Jansen R, Gerstein M. Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics, 2002,18(4): 585-596.
  • 5Sanchez-Palencia A, Gomez-Morales M, Gomez-Capilla JA, et al. Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer. Int J Cancer, 2011, 129(2): 355-364.
  • 6Hou J, Aerts J, den Hamer B, et al. Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PloS One, 2010, 5(4): e10312.
  • 7Su LJ, Chang Cw, Wu YC, et al. Selection ofDDX5 as a novel internal control for Q-RT- PCR from microarray data using a block bootstrap re-sampling scheme. BMC Genomics, 2007, S: 140.
  • 8Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol, 2004,5(10): R80.
  • 9Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003,4(2): 249-264.
  • 10Gautier L, Cope L, Bolstad BM, et al. affy-analysis of Affymetrix GeneChip data atthe probe level. Bioinformatics,2004,20(3): 307-315.

同被引文献25

  • 1Jemal A, Bray F, Center MM, et al. Global cancer statistics [J]. CA Cancer J Clin,2011,61(2) :69 -90.
  • 2Lazar V, Garcia JG. A single human myosin light chain ki- nase gene ( MLCK; MYLK ) [ J ]. Genomics, 1999,57 ( 2 ): 256 - 267.
  • 3Somlyo AP, Somlyo AV. Ca2 sensitivity of smooth muscle and nonmuscle myosin 11 :modulated by G proteins, kina- ses, and myosin phosphatase [ J ]. Physiol Rev, 2003,83 (4) :1 325 -1 358.
  • 4Hong F, Haldeman BD, Jackson D, et al. Biochemistry of smooth muscle myosin light chain kinase [ J ]. Arch Biochem Biophys,2011,510(2) :135 - 146.
  • 5Han YJ, Ma SF, Yourek G, et al. A transcribed pseudogene of MYLK promotes cell proliferation [ J ]. FASEB J, 2011, 25(7) :2 305 -2 312.
  • 6Lee WS, Seo G, Shin H J, et al. Identification of differential- ly expressed genes in microsatellite stable HNPCC and spo- radic colon cancer[ J]. J Surg Res,2008,144( 1 ):29 -35.
  • 7Chen L, Su L, Li J, et al. Hypermethylated FAMSC and MYLK in serum as diagnosis and pre-warning markers for gastric cancer[J]. Dis Markers,2012,32(3) :195 -202.
  • 8Minamiya Y, Nakagawa T, Saito H, et al. Increased expres- sion of myosin light chain kinase mRNA is related to metas- tasis in non-small cell lung cancer[ J]. Turnout Biol,2005, 26(3) :153 - 157.
  • 9Jemal A,Bray F,Center MM. Global cancer statistics[J].CA:A Cancer Journal for Clinicians,2011,(02):69-90.
  • 10Chen L,Su L,Li J. Hypermethylated FAM5C and MYLK in serum as diagnosis and pre-warning markers for gastric cancer[J].Disease Markers,2012,(03):195-202.

引证文献3

二级引证文献8

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部